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MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning
Reinforcement learning (RL) is widely used in language-model post-training to maximize rewards assigned to individual model outputs, such as scores from binary verifiers or reward models trained on human feedback. However, applications such as synthetic-data generation, fairness-related constraint s
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